compounding-intelligence/scripts/session_pair_harvester.py

#!/usr/bin/env python3
"""
Session Transcript → Training Pair Harvester

Scans Hermes session JSONL files for Q&A patterns and extracts
terse→rich training pairs. Outputs JSONL matching the timmy-config
training pairs spec.

Usage:
  python3 scripts/session_pair_harvester.py ~/.hermes/sessions/
  python3 scripts/session_pair_harvester.py session.jsonl --output pairs.jsonl
  python3 scripts/session_pair_harvester.py --dir ~/.hermes/sessions/ --min-ratio 2.0

Output format:
  {"terse": "user short prompt", "rich": "ai detailed response", "source": "session_id", "model": "..."}
"""

import argparse
import hashlib
import json
import sys
from pathlib import Path
from typing import Optional


def compute_hash(text: str) -> str:
    """Content hash for deduplication."""
    return hashlib.sha256(text.encode()).hexdigest()[:16]


def extract_pairs_from_session(session_data: dict, min_ratio: float = 1.5,
                                min_response_words: int = 20) -> list:
    """Extract terse→rich pairs from a single session object."""
    pairs = []
    conversations = session_data.get("conversations", [])
    session_id = session_data.get("id", "unknown")
    model = session_data.get("model", "unknown")

    seen_hashes = set()

    for i, msg in enumerate(conversations):
        # Look for assistant/gpt responses
        if msg.get("from") not in ("gpt", "assistant"):
            continue

        response_text = msg.get("value", "")
        if not response_text or len(response_text.split()) < min_response_words:
            continue

        # Find the preceding human message
        prompt_text = ""
        for j in range(i - 1, -1, -1):
            if conversations[j].get("from") == "human":
                prompt_text = conversations[j].get("value", "")
                break

        if not prompt_text:
            continue

        # Filter: skip tool results, system messages embedded as human
        if prompt_text.startswith("{") and "output" in prompt_text[:100]:
            continue  # likely a tool result
        if prompt_text.startswith("# SOUL.md") or prompt_text.startswith("You are"):
            continue  # system prompt leak

        # Quality filters
        prompt_words = len(prompt_text.split())
        response_words = len(response_text.split())

        # Must have meaningful length ratio
        if prompt_words == 0 or response_words == 0:
            continue
        ratio = response_words / prompt_words
        if ratio < min_ratio:
            continue

        # Skip responses that are mostly code
        code_blocks = response_text.count("```")
        if code_blocks >= 4 and len(response_text.replace("```", "").strip()) < 50:
            continue

        # Skip responses with tool call artifacts
        if "tool_call" in response_text[:100] or "function_call" in response_text[:100]:
            continue

        # Deduplicate by content hash
        content_hash = compute_hash(prompt_text + response_text[:200])
        if content_hash in seen_hashes:
            continue
        seen_hashes.add(content_hash)

        # Clean up response: remove markdown headers if too many
        clean_response = response_text

        pairs.append({
            "terse": prompt_text.strip(),
            "rich": clean_response.strip(),
            "source": session_id,
            "model": model,
            "prompt_words": prompt_words,
            "response_words": response_words,
            "ratio": round(ratio, 2),
        })

    return pairs


def extract_from_jsonl_file(filepath: str, **kwargs) -> list:
    """Extract pairs from a session JSONL file."""
    pairs = []
    path = Path(filepath)

    if not path.exists():
        print(f"Warning: {filepath} not found", file=sys.stderr)
        return pairs

    content = path.read_text()
    lines = content.strip().split("\n")

    for line in lines:
        line = line.strip()
        if not line:
            continue
        try:
            session = json.loads(line)
        except json.JSONDecodeError:
            continue

        session_pairs = extract_pairs_from_session(session, **kwargs)
        pairs.extend(session_pairs)

    return pairs


def deduplicate_pairs(pairs: list) -> list:
    """Remove duplicate pairs across files."""
    seen = set()
    unique = []
    for pair in pairs:
        key = compute_hash(pair["terse"] + pair["rich"][:200])
        if key not in seen:
            seen.add(key)
            unique.append(pair)
    return unique


def main():
    parser = argparse.ArgumentParser(description="Harvest training pairs from session transcripts")
    parser.add_argument("input", nargs="?", help="Session JSONL file or directory")
    parser.add_argument("--dir", "-d", help="Directory to scan for session files")
    parser.add_argument("--output", "-o", default="harvested_pairs.jsonl", help="Output file")
    parser.add_argument("--min-ratio", type=float, default=1.5, help="Min response/prompt word ratio")
    parser.add_argument("--min-words", type=int, default=20, help="Min response word count")
    parser.add_argument("--dry-run", action="store_true", help="Print stats without writing")
    args = parser.parse_args()

    all_pairs = []
    files_scanned = 0

    scan_dir = args.dir or args.input
    if not scan_dir:
        parser.print_help()
        sys.exit(1)

    scan_path = Path(scan_dir)
    if scan_path.is_dir():
        jsonl_files = sorted(scan_path.rglob("*.jsonl"))
        print(f"Scanning {len(jsonl_files)} files in {scan_dir}...", file=sys.stderr)
        for fpath in jsonl_files:
            pairs = extract_from_jsonl_file(
                str(fpath),
                min_ratio=args.min_ratio,
                min_response_words=args.min_words
            )
            all_pairs.extend(pairs)
            files_scanned += 1
    else:
        pairs = extract_from_jsonl_file(
            str(scan_path),
            min_ratio=args.min_ratio,
            min_response_words=args.min_words
        )
        all_pairs.extend(pairs)
        files_scanned = 1

    # Deduplicate
    unique_pairs = deduplicate_pairs(all_pairs)

    # Stats
    if unique_pairs:
        avg_prompt = sum(p["prompt_words"] for p in unique_pairs) / len(unique_pairs)
        avg_response = sum(p["response_words"] for p in unique_pairs) / len(unique_pairs)
        avg_ratio = sum(p["ratio"] for p in unique_pairs) / len(unique_pairs)
    else:
        avg_prompt = avg_response = avg_ratio = 0

    stats = {
        "files_scanned": files_scanned,
        "raw_pairs": len(all_pairs),
        "unique_pairs": len(unique_pairs),
        "duplicates_removed": len(all_pairs) - len(unique_pairs),
        "avg_prompt_words": round(avg_prompt, 1),
        "avg_response_words": round(avg_response, 1),
        "avg_ratio": round(avg_ratio, 2),
    }

    print(json.dumps(stats, indent=2), file=sys.stderr)

    if args.dry_run:
        # Print sample pairs
        for pair in unique_pairs[:3]:
            print(f"\n--- Source: {pair['source']} (ratio: {pair['ratio']}) ---", file=sys.stderr)
            print(f"TERSE: {pair['terse'][:100]}...", file=sys.stderr)
            print(f"RICH: {pair['rich'][:150]}...", file=sys.stderr)
        return

    # Write output
    output_path = Path(args.output)
    with open(output_path, "w") as f:
        for pair in unique_pairs:
            # Strip internal fields for output
            output = {
                "terse": pair["terse"],
                "rich": pair["rich"],
                "source": pair["source"],
                "model": pair["model"],
            }
            f.write(json.dumps(output) + "\n")

    print(f"\nWrote {len(unique_pairs)} pairs to {output_path}", file=sys.stderr)


if __name__ == "__main__":
    main()